Public health studies how communities prevent disease, promote health, and respond to health threats. Epidemiology is the part of public health that measures how diseases spread and which factors increase risk. This cheat sheet helps students organize the most important formulas, study designs, and prevention ideas used in medical science.
It is useful for comparing disease patterns, interpreting data, and understanding real public health decisions.
Core epidemiology focuses on person, place, time, exposure, outcome, and population. Important calculations include incidence, prevalence, mortality rate, case fatality rate, relative risk, odds ratio, and basic reproduction number. Students should also understand screening accuracy, including sensitivity, specificity, false positives, and false negatives.
Public health action connects data to prevention through surveillance, vaccination, sanitation, education, policy, and outbreak control.
Key Facts
- Incidence rate = new cases during a time period / population at risk during that time period.
- Prevalence = total existing cases / total population at a specific time.
- Mortality rate = deaths from a cause during a time period / total population during that time period.
- Case fatality rate = deaths from a disease / diagnosed cases of that disease.
- Relative risk = incidence in exposed group / incidence in unexposed group.
- Basic reproduction number R0 is the average number of new infections caused by one infected person in a fully susceptible population.
- Herd immunity threshold = 1 - 1/R0, which estimates the fraction of a population needing immunity to slow spread.
- Sensitivity = true positives / all people who actually have the disease, and specificity = true negatives / all people who do not have the disease.
Vocabulary
- Epidemiology
- Epidemiology is the study of how diseases and health conditions are distributed in populations and what factors influence them.
- Incidence
- Incidence is the number or rate of new cases of a disease that occur in a population during a specific time period.
- Prevalence
- Prevalence is the total proportion of a population that has a disease or condition at a specific time.
- Outbreak
- An outbreak is a sudden increase in cases of a disease above what is normally expected in a place or group.
- Risk Factor
- A risk factor is a characteristic, behavior, exposure, or condition that increases the chance of developing a disease.
- Screening Test
- A screening test is used to identify people who may have a disease before symptoms appear or before a diagnosis is confirmed.
Common Mistakes to Avoid
- Confusing incidence with prevalence is wrong because incidence counts only new cases, while prevalence counts all existing cases.
- Interpreting correlation as causation is wrong because two variables can be linked without one directly causing the other.
- Using the total population instead of the population at risk is wrong because incidence should only include people who could develop the disease.
- Ignoring false positives and false negatives is wrong because screening tests are not perfect and results must be interpreted using sensitivity and specificity.
- Comparing raw case counts between places is wrong when population sizes differ because rates give a fairer comparison.
Practice Questions
- 1 A town of 20,000 people reports 80 new cases of flu in one month. What is the monthly incidence rate per 1,000 people?
- 2 In a school of 1,200 students, 60 students currently have asthma. What is the prevalence of asthma as a percent?
- 3 In a study, 30 of 300 exposed people develop an illness, while 10 of 400 unexposed people develop it. What is the relative risk?
- 4 Why might public health officials recommend vaccination even when many individuals have only a low personal risk of severe disease?
Understanding Public Health & Epidemiology
A rate is only meaningful when its denominator matches the group being studied. For example, people who have had an organ removed may no longer be at risk for a disease in that organ. Age matters too.
A community with many older residents may show more deaths than a younger community even when the underlying health conditions are similar. Epidemiologists often compare age adjusted rates to make fairer comparisons. They may calculate rates by neighborhood, school year, job type, or season.
A graph can reveal a sudden rise, but it cannot by itself explain the cause. Data quality matters because missed diagnoses, delayed reports, and unequal access to testing can change the apparent pattern.
Finding a link between an exposure and an outcome does not prove that the exposure caused the outcome. A confounder is a third factor connected to both. For instance, a study might find that coffee drinkers have a higher disease rate, while smoking is the real factor increasing risk if many coffee drinkers smoke.
Randomized controlled trials reduce some sources of bias because researchers assign treatments by chance. They are not always possible or ethical. Cohort studies follow groups over time and can measure new cases.
Case control studies begin with people who have an outcome, then examine earlier exposures. Cross sectional studies provide a snapshot, which is useful for prevalence but weaker for showing which event came first.
Disease spread changes as conditions change. The basic reproduction number describes spread in a fully susceptible population, but real populations have immunity, behavior changes, and uneven contact patterns. The effective reproduction number is more useful during an outbreak because it reflects current conditions.
If it stays above one, case numbers tend to grow. If it falls below one, spread tends to shrink. Contact networks matter greatly.
A virus may move quickly through crowded homes, teams, dormitories, or workplaces while spreading slowly between separate groups. Isolation of cases, ventilation, staying home when ill, vaccination, and rapid notification of close contacts can each reduce opportunities for transmission.
Screening tests are used in people who may feel healthy, so test results need careful interpretation. Sensitivity and specificity describe test performance, but a person usually wants to know how likely a positive result means real disease. That depends strongly on how common the disease is in the tested group.
When a rare condition is screened in a large population, even a highly specific test can produce many false positives. Confirmatory testing helps prevent unnecessary worry or treatment. Public health decisions must weigh benefits against harms, including cost, privacy, stigma, and unequal access.
During outbreak investigations, teams first confirm that cases are real, define who counts as a case, map the time and location of illness, form a hypothesis, then test it with evidence. Good conclusions remain open to revision when stronger data appear.